WO2009142744A2 - Prédiction du risque hémostatique en fonction de la composition du plasma - Google Patents

Prédiction du risque hémostatique en fonction de la composition du plasma Download PDF

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WO2009142744A2
WO2009142744A2 PCT/US2009/003148 US2009003148W WO2009142744A2 WO 2009142744 A2 WO2009142744 A2 WO 2009142744A2 US 2009003148 W US2009003148 W US 2009003148W WO 2009142744 A2 WO2009142744 A2 WO 2009142744A2
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factor
thrombin
blood
concentration
subject
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PCT/US2009/003148
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WO2009142744A3 (fr
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Kenneth G. Mann
Kathleen Brummel Ziedins
Thomas Orfeo
Matthew F. Hockin
Stephen J. Everse
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The University Of Vermont And State Agriculture College
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Publication of WO2009142744A2 publication Critical patent/WO2009142744A2/fr
Publication of WO2009142744A3 publication Critical patent/WO2009142744A3/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/86—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving blood coagulating time or factors, or their receptors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/22—Haematology
    • G01N2800/224—Haemostasis or coagulation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/50—Determining the risk of developing a disease

Definitions

  • the present invention relates to methods for assessing and determining hemostatic risk and more particularly to methods for using numerical techniques for simulating in silico blood clotting reactions as a mechanism for such assessing and determining.
  • thrombin is the central event of the blood coagulation process, essential for hemostasis and the culprit in thrombosis [Mann, K. G., Butenas, S., and Brummel, K. E. (2003) Arterioscler.Thromb. Vasc.Biol. 23, 17-25; Mann, K. G., Brummel, K., and Butenas, S. (2003) Journal of Thrombosis and Haemostasis 1, 1504-1514]. Congenital diseases associated with the absence or reduced production of thrombin
  • thromboin's contribution to venous thrombosis is clearly paramount as evidenced by abundant clinical data demonstrating the efficacy of direct or indirect thrombin inhibitors in primary and secondary venous thromboembolic prophylaxis [Kwaan, H. C. and Samama, M. M.
  • VTE venous thromboembolism
  • Vossen et al. showed that hereditary thrombophilia can contribute to the risk of arterial disease; thus relating specific defects in natural anticoagulant systems that regulate thrombin generation in venous thrombosis to arterial disease [Vossen, C. Y. and Rosendaal, F. R. (2006) J.Thromb.Haemost. 4, 916-918].
  • the complex catalysts which participate in the generation of thrombin via the Tf pathway are composed of a serine protease interacting with a receptor/cofactor protein, which are anchored to a discreet surface. In most cases the surface is the membrane of an activated cell.
  • the key-initiating event in the generation of thrombin depends upon the interaction of normally cryptic, membrane bound, Tf with plasma fVTla. The latter is preexistent in blood at approximately 1-2% of the total fVTl concentration (1OnM). While the source and presentation of active Tf is controversial, the damage or cytokine-related presentation of the active Tf trigger for the process is essential.
  • Plasma fVTla appears to possess the appropriate catalytic machinery to display the active site of an effective serine protease, but does not express proteolytic activity unless it is bound to Tf on a membrane.
  • naked fVTJa at natural biological concentrations has no significant activity toward either fEX or fX prior to binding to Tf.
  • the defective active site also allows fVIIa to escape inhibition by the high concentrations of antithrombin-III (AT-IU) and other inhibitors present in blood.
  • AT-IU antithrombin-III
  • the fV ⁇ a-Tf protein-protein interaction switches on the active site of fVTfa by increasing the kcat of the enzyme and increases the rate of fX activation by four orders of magnitude.
  • the fIX zymogen is a competitive substrate with fX and requires two peptide bond cleavages (R145, Rl 80) for activity. While both of these cleavages are catalyzed by fVIIa-Tf, fXa, bound to a membrane, they can provide one of the two required cleavages (R145) to produce the intermediate fIX ⁇ . Thus, this feedback cleavage by membrane-bound fXa enhances the rate of generation of ffXa, which is completed with the second bond cleavage (Rl 80) by fV ⁇ a-Tf.
  • the initial fXa produced when bound to a membrane activates small (pM) amounts of prothrombin to thrombin, albeit rather inefficiently.
  • This initial thrombin is essential to the acceleration of the process by serving as the activator for platelets, fV and fVTn.
  • the ffXa generated by fVUa-Tf combines with fVTfla on the activated platelet membrane to form the "intrinsic factor Xase" which becomes the major activator of fX.
  • frXa-fVTna complex is 10 5 - 10 6 -fold more active than fDCa alone as a fX activator and ⁇ 50-fold more efficient than fVTfa-Tf in catalyzing fX activation.
  • fVTfa-Tf is under the control of Tf pathway inhibitor (TFPI); thus, the bulk of fXa is ultimately produced by fTXa-fVUIa.
  • FXa combines with fVa on activated platelet membrane surfaces at specific receptor sites and this fXa-fVa "prothrombinase" catalyst converts prothrombin to thrombin.
  • Prothrombinase is ⁇ 300,000-fold more active than fXa alone in catalyzing prothrombin activation.
  • the coagulation system is under tight regulation by stoichiometric and dynamic inhibition systems.
  • the Tf concentration threshold for reaction initiation is steep and the ultimate amount of thrombin produced is largely regulated by the concentrations of plasma procoagulants and the stoichiometric inhibitors TFPI and AT-III and the constituents of the dynamic inhibition processes.
  • TFPI function eliminates the initial production of fXa by the
  • exosic factor Xase the low abundance ( ⁇ 2.5 nM), high affinity TFPI, however, can only delay the hemostatic reaction.
  • the TFPI in blood is increased by heparin, which releases the endothelial cell bound inhibitor.
  • the lower affinity, stoichiometric inhibitor AT-Hl is normally present in plasma at over twice the concentration (3.4 ⁇ M) of any potential target enzyme generated by the Tf pathway.
  • AT-III is an effective neutralizer of all the procoagulant serine proteases.
  • the targets of AT-III are primarily the uncomplexed enzyme products of these reactions.
  • ⁇ 2 -macroglobulin ( ⁇ 2 M) is also reported to account for some thrombin inhibition, ⁇ i -antitrypsin ( ⁇ iAT) probably also contributes.
  • thrombin binds to constitutively present vascular thrombomodulin (Tm) and activates PC, which is presented by the endothelial cell PC receptor (EPCR), to its activated species APC.
  • EPCR endothelial cell PC receptor
  • APC competitively binds with both fVTfla and fVa interfering with the formation of the "intrinsic factor Xase” and the "prothrombinase", initially by competition with fXa and fDCa and ultimately by cleaving the cofactors to eliminate these complexes.
  • the combinations of TFPI, the PC system and AT-III cooperate to produce steep Tf concentration thresholds, acting like digital "switches", allowing or blocking significant thrombin formation.
  • the delay incurred by TFPI and the slower inhibitions by AT-III and the APC system control the level of Tf required to overcome the reaction threshold.
  • TFPI is releasable while Tm is constitutively present on the vascular endothelium.
  • the relative concentrations of these two important regulators throughout the vascular system remain ambiguous.
  • fXI has little or no effect on thrombin generation in whole blood in vitro.
  • FXI knockout mice do not display spontaneous hemorrhagic pathology. The variability of pathology in humans is most likely a reflection of the nature and extent of the vascular lesion in fXI deficient individuals. While fXH, prekallekrein and high molecular weight kininogen do not appear to be fundamental to the process of hemostasis, the contributions of contact pathway elements to coagulation and fibrinolysis remain open, thus requiring further experimentation to resolve these issues, especially in the pathology of thrombosis. Protein S (PS) is a component not appropriately dealt with in present reconstruction studies.
  • PS Protein S
  • PS deficiency is clearly a grave prothrombotic state. Infants identified with homozygous PS deficiency do not survive, succumbing to purpura fulminans shortly after birth. However, during the 25 years following its discovery, only a poor and controversial understanding of PS function has been developed. PS has cofactor activity for APC, enhancing cleavage at R306 in fVa. It has also been proposed to be a direct inhibitor of fXa and "prothrombinase". It was proposed to be a membrane inhibitor. We have confirmed that conclusion in studies that showed the molecule would inhibit prothrombin activation, with inhibition alleviated by phospholipids or platelets.
  • PZ protein Z
  • Active heparin molecules constituting 30-40% of UFH preparations, bind to and conformationally activate the stoichiometric plasma protease inhibitor antithrombin (AT), increasing its reactivity with a number of catalysts critical to the procoagulant response, including thrombin ( ⁇ 10 3 fold), factor(f) Xa ( ⁇ 10 2 to 10 4 fold ), and fIXa ( ⁇ 10 6 fold).
  • AT stoichiometric plasma protease inhibitor antithrombin
  • thrombin ⁇ 10 3 fold
  • factor(f) Xa ⁇ 10 2 to 10 4 fold
  • fIXa ⁇ 10 6 fold
  • the achieved level of UFH anticoagulation reflects the percentage of the circulating AT pool that is complexed with heparin. For example, plasma levels of UFH considered to be appropriate in some acute settings, from 0.35 to 0.7 U/mL (12), represent "activation" of only 5 to 10% of the circulating AT pool.
  • fragmented heparin preparations include fragmented heparin preparations, synthetic heparin analogues, molecules that directly target and inhibit specific procoagulant catalysts such as thrombin, fXa, tissue factor (Tfj-fVIIa, and fJXa and agents that enhance endogenous fibrinolysis.
  • procoagulant catalysts such as thrombin, fXa, tissue factor (Tfj-fVIIa, and fJXa
  • agents that enhance endogenous fibrinolysis include fragmented heparin preparations, synthetic heparin analogues, molecules that directly target and inhibit specific procoagulant catalysts such as thrombin, fXa, tissue factor (Tfj-fVIIa, and fJXa and agents that enhance endogenous fibrinolysis.
  • Anticoagulant therapy is used in two broadly defined settings: prophylactic, where the goal is the suppression of anticipated episodes of Tf-initiated coagulation; and therapeutic, where the goal is the suppression of ongoing (already established) coagulation processes.
  • prophylactic where the goal is the suppression of anticipated episodes of Tf-initiated coagulation
  • therapeutic where the goal is the suppression of ongoing (already established) coagulation processes.
  • the transition between prophylactic and therapeutic applications is accomplished by adjustment of its plasma concentration.
  • a given anticoagulant and accompanying regimen is recommended for use in only one of these settings, with additional stratification indicating use with specific patient populations or surgical interventions.
  • Improved in vitro and/or computational methods that could predict relative success in prophylactic or therapeutic settings prior to animal and clinical studies or that anticipated specific usage limitations would be useful.
  • mathematical modeling and computer simulations are tools for the prediction of the behavior of the blood coagulation system and an individual's response to challenge.
  • the basic idea is that in any individual, procoagulant and anticoagulant factor levels together act to generate a unique coagulation profile (or phenotype) and that mathematical modeling represent a methodology for defining these phenotypes.
  • the correct ensemble of procoagulant and anticoagulant proteins is identified, the thermodynamic, stoichiometric, and catalytic parameters characterizing their interactions are determined, and the relevant physiologic triggers are identified.
  • These modeling efforts exhibits an ability to adequately describe biochemical events in two closed experimental systems: the well-defined synthetic coagulation proteome and the minimally altered phlebotomy whole blood.
  • Virchow's triad describes the factors influencing thrombosis: the vessel wall, blood and its flow.
  • blood has been shown to be a clinical predictor of human health and disease. Though complex in its composition, blood is readily accessible and has been characterized extensively. Therefore, mathematical modeling and computer simulations are useful methodologies to adopt in an effort to characterize the etiology of bleeding and thrombosis.
  • the levels of an individual's proteins in blood at a given time reflect the sum of developmental, environmental, genetic, nutritional, and pharmacological events.
  • the resulting ensemble of specific protein levels yields a characteristic phenotype that is representative of the in vivo performance of an individual's hemostatic system when it is challenged.
  • the present inventions also proceed from the basis that a limited array of coagulation factors, all of which are at levels considered clinically normal, can be integrated by a computational model to predict an individual's potential to generate thrombin and thus be related to that individual's hemostatic risk in a variety of settings.
  • acquired data indicates that hypothetical thrombin generation based upon coagulation factor composition can distinguish between ACS and CAD.
  • Individuals within the ACS population generated significantly higher levels of thrombin at a 50% faster rate resulting in more thrombin over the time course of the TF-initiated coagulation reaction.
  • prothrombotic thrombin generation profiles of individuals with ACS appear to be driven primarily by collective alterations in factor VIII, antithrombin and prothrombin levels.
  • a method for assessing or determining hemostatic risk of a subject by simulating, in silico, the concentration of thrombin based on biological input of a sample taken from a subject and comparing the results of such a simulation with a reference and determining the presence of hemostatic risk based on such results.
  • the risk: assessment is made in the present invention before the onset of an ACS event.
  • a determination is made as to the risk that another such event could occur because of the presence of factors that indicate that the subject is predisposed for such another occurrence.
  • prophylactic treatment can be determined based on the simulation. It also is within the scope of the present invention, as described herein, to develop treatment after the fact as well.
  • a method for assessing or determining hemostatic risk of a subject includes determining the concentrations of a plurality of blood factors in a biologic sample from the subject and simulating in silico the concentration of thrombin from the determined concentrations. Such a method further includes comparing the simulated concentration of thrombin to a reference by a clinician, and determining from the simulated concentration if the subject is predisposed to hemostatic risk.
  • the hemostatic risk is one of ACS or hemophilia.
  • such determining includes determining the concentrations of three blood factors in a biologic sample from the subject.
  • the blood factors are selected from the group consisting of AT, FII, FVm, Protein C, Protein S, Factor y L ⁇ dea ) and tissue factor pathway inhibitor (TFPI).
  • the three blood factors are one of (a) AT, FII, and FVIII; (b) Factor V Uiden , Protein C and Protein S or (c) TFPI, AT and FVHI.
  • such a method further includes, after determining that the subject is predisposed to hemostatic risk, determining a prophylactic treatment to minimize the hemostatic risk to the subject.
  • such a method further includes evaluating the efficacy of the determined prophylactic treatment.
  • such methods further includes, after determining the prophylactic treatment, inputting parameters representative of the capacity of the prophylactic treatment to modulate at least one blood factor and repeating said simulating in silico the concentration of thrombin from the determined concentrations and the modulating effect of the determined prophylactic treatment.
  • Such methods also include assessing by the clinician the efficacy of the determined prophylactic treatment to minimize the hemostatic risk and (i) if said assessment provides a satisfactory indication of efficacy, proscribing the prophylactic treatment; and (ii) if said assessment provides an unsatisfactory indication of efficacy; selecting another prophylactic treatment and repeating said steps of inputting, said repeating I said simulating and said assessing for the another prophylactic treatment.
  • Such a prophylactic treatment is at least one of drugs, medicaments, dietary and physical therapy.
  • the drug modulates at least one of the procoagulant factor or the anticoagulant factor of the subject.
  • such simulating in silico includes performing a series of computer executable functions that manipulate input data featuring at least one of blood coagulation formation, expression and propagation variables, the functions generating, as output, a thrombin concentration, wherein the amount of thrombin is taken to be indicative of the blood coagulation.
  • the computer executable functions include at least one of the following variables: 1) TFPI mediated inactivation of TF* Vila and its product complexes; 2) AT-HI mediated inactivation of Ha, mlla, factor Vila, factor DCa, and factor Xa; 3) initial activation of factor V and factor VIII by thrombin generated by factor Xa-membrane; 4) factor V inactivation by activated Protein C pathway; 5) factor VHIa dissociation/activity loss; 6) binding competition, and kinetic activation steps which exist between tissue factor (TF) and factors VII and Vila, and 7) activation of factor VII by Ha, factor Xa, and factor IXa.
  • variables 1) TFPI mediated inactivation of TF* Vila and its product complexes; 2) AT-HI mediated inactivation of Ha, mlla, factor Vila, factor DCa, and factor Xa; 3) initial activation of factor V and factor VIII by thrombin generated by factor Xa-membrane;
  • the hemostatic risk is ACS
  • comparing includes comparing the simulated concentration of thrombin to a reference by the clinician, and determining from the simulated concentration if the subject is predisposed to ACS, wherein a simulated thrombin concentration within one standard deviation of the reference indicates that the subject is predisposed to ACS.
  • such determining includes determining the concentrations of three blood factors in a biologic sample from the subject, where the blood factors are selected from the group consisting of AT, FII, FVIII, Protein C, Protein S, Factor y Leiden ⁇ an( j t j ssue f ac tor pathway inhibitor (TFPI).
  • the three blood factors are AT, FII, and FVIH.
  • such a method further includes, after determining that the subject is predisposed to hemostatic risk, determining a prophylactic treatment to minimize the hemostatic risk to the subject, hi yet further embodiments, such a method further includes evaluating the efficacy of the determined prophylactic treatment. More particularly, such methods further includes, after determining the prophylactic treatment, inputting parameters representative of the capacity of the prophylactic treatment to modulate at least one blood factor and repeating said simulating in silico the concentration of thrombin from the determined concentrations and the modulating effect of the determined prophylactic treatment.
  • Such methods also include assessing by the clinician the efficacy of the determined prophylactic treatment to minimize the hemostatic risk and (i) if such assessing provides a satisfactory indication of efficacy, proscribing the prophylactic treatment; and (ii) if such assessing provides an unsatisfactory indication of efficacy; selecting another prophylactic treatment and repeating said steps of inputting, said repeating said simulating and said assessing for the another prophylactic treatment.
  • Such a prophylactic treatment is at least one of drugs, medicaments, dietary and physical therapy.
  • the drug modulates at least one of the procoagulant factor or the anticoagulant factor of the subject.
  • simulating in silico includes performing a series of computer executable functions that manipulate input data featuring at least one of blood coagulation formation, expression and propagation variables, the functions generating, as output, a thrombin concentration, wherein the amount of thrombin is taken to be indicative of the blood coagulation.
  • the computer executable functions include at least one of the following variables: 1) TFPI mediated inactivation of TF* Vila and its product complexes; 2) AT-IH mediated inactivation of Ha, mHa, factor VTIa, factor IXa, and factor Xa; 3) initial activation of factor V and factor VTTI by thrombin generated by factor Xa-membrane; 4) factor V inactivation by activated Protein C pathway; 5) factor VHIa dissociation/activity loss; 6) binding competition, and kinetic activation steps which exist between tissue factor (TF) and factors VII and Vila, and 7) activation of factor VII by Ha, factor Xa, and factor IXa.
  • variables include at least one of the following variables: 1) TFPI mediated inactivation of TF* Vila and its product complexes; 2) AT-IH mediated inactivation of Ha, mHa, factor VTIa, factor IXa, and factor Xa; 3) initial activation of factor V and factor
  • such methods include a method for diagnosing ACS in a subject, that includes determining the concentrations of AT, FII, and FVm in a biologic sample from the subject and simulating, in silico, the concentration of thrombin from the concentrations of AT, FII, and FVIII. Such a method also includes comparing the simulated concentration of thrombin to a reference, wherein a simulated thrombin concentration within one Standard deviation of the reference indicates that the subject has ACS.
  • the clinician can determine if the subject is predisposed to the occurrence of a medical condition such as ACS or hemophilia and proscribe a prophylactic treatment protocol to minimize the hemostatic risk to the subject.
  • a medical condition such as ACS or hemophilia
  • such methods include a method for selecting a treatment in a subject.
  • a method for selecting a treatment in a subject includes, simulating in silico the concentration of thrombin from the concentrations of a plurality of blood factors in a biologic sample from the subject; comparing the simulated concentration of thrombin to a reference; and selecting a treatment based on said comparing.
  • such determining includes determining the concentrations of three blood factors in a biologic sample from the subject.
  • the blood factors are selected from the group consisting of AT, FII, FVm, Protein C, Protein S, Factor V Leiden , and tissue factor pathway inhibitor (TFPI).
  • the three blood factors are one of (a) AT, FII, and FVHI; (b) Factor V Ulden , Protein C and Protein S or (c) TFPI, AT and FVTfl.
  • such a method further includes evaluating the efficacy of the determined prophylactic treatment.
  • such methods further includes, after determining the treatment, inputting parameters representative of the capacity of the treatment to modulate at least one blood factor and repeating said simulating in silico the concentration of thrombin from the determined concentrations and the modulating effect of the determined treatment.
  • Such a method also include assessing by the clinician the efficacy of the determined treatment and (i) if such assessing provides a satisfactory indication of efficacy, proscribing the treatment; and (ii) if such assessing provides an unsatisfactory indication of efficacy; selecting another treatment and repeating said steps of inputting, said repeating said simulating and said assessing for the another treatment.
  • a treatment is at least one of drugs, medicaments, dietary and physical therapy.
  • the drug modulates at least one of the procoagulant factor or the anticoagulant factor of the subject.
  • simulating in silico includes performing a series of computer executable functions that manipulate input data featuring at least one of blood coagulation formation, expression and propagation variables, the functions generating, as output, a thrombin concentration, wherein the amount of thrombin is taken to be indicative of the blood coagulation.
  • the computer executable functions include at least one of the following variables: 1) TFPI mediated inactivation of TF* Vila and its product complexes; 2) AT-i ⁇ mediated inactivation of Eta, mlla, factor Vila, factor IXa, and factor Xa; 3) initial activation of factor V and factor VED[ by thrombin generated by factor Xa-membrane; 4) factor V inactivation by activated Protein C pathway; 5) factor VEHa dissociation/activity loss; 6) binding competition, and kinetic activation steps which exist between tissue factor (TF) and factors VII and Vila, and 7) activation of factor VEl by Eta, factor Xa, and factor LXa.
  • variables 1) TFPI mediated inactivation of TF* Vila and its product complexes; 2) AT-i ⁇ mediated inactivation of Eta, mlla, factor Vila, factor IXa, and factor Xa; 3) initial activation of factor V and factor VED[ by thro
  • such methods further include a method for selecting a treatment in a subject, where such a method includes simulating the concentration of thrombin from the concentrations of AT, FII, and FVEEt in a biologic sample from the subject and comparing the simulated concentration of thrombin to a reference.
  • Such a method further includes selecting a treatment based on such comparing.
  • simulating in silico includes performing a series of computer executable functions that manipulate input data featuring at least one of blood coagulation formation, expression and propagation variables, the functions generating, as output, a thrombin concentration, wherein the amount of thrombin is taken to be indicative of the blood coagulation.
  • such method further includes assessing the efficacy of drugs, medicaments and other therapy known to those skilled in the art to provide the expected prophylactic treatment and thus minimize the hemostatic risk.
  • assessing efficacy includes a methodology for screening, in silco, candidate compounds for ability to prevent or treat the underlying medical condition blood clotting.
  • Such a methodology includes pre-selecting a drug, medicament or the like for capacity to modulate at least one blood factor, performing a series of computer executable functions that manipulate input data featuring at least one of blood coagulation formation, expression and propagation variables, the functions generating, as output, a thrombin concentration, wherein the amount of thrombin produced by the drug is taken to be indicative of a compound that prevents or treats the medical condition.
  • the computer executable functions include at least one of the following variables: 1) TFPI mediated inactivation of TF* Vila and its product complexes; 2) AT-i ⁇ mediated inactivation of Ha, mlla, factor Vila, factor DCa, and factor Xa; 3) initial activation of factor V and factor VIII by thrombin generated by factor Xa-membrane; 4) factor V inactivation by activated Protein C pathway; 5) factor VIHa dissociation/activity loss; 6) binding competition, and kinetic activation steps which exist between tissue factor (TF) and factors VII and VTIa, and 7) activation of factor VII by Ha, factor Xa, and factor IXa.
  • variables 1) TFPI mediated inactivation of TF* Vila and its product complexes; 2) AT-i ⁇ mediated inactivation of Ha, mlla, factor Vila, factor DCa, and factor Xa; 3) initial activation of factor V and factor VIII by thrombin generated by factor Xa-membr
  • ACS shall be understood to mean or refer to acute coronary syndrome.
  • APC shall be understood to mean or relate to the activated species of protein C.
  • CAD shall be understood to mean or refer to stable coronary artery disease.
  • Computer readable medium shall be understood to mean any article of manufacture that contains data that can be read by a computer or a carrier wave signal carrying data that can be read by a computer.
  • Such computer readable media includes but is not limited to magnetic media, such as a floppy disk, a flexible disk, a hard disk, reel-to-reel tape, cartridge tape, cassette tape or cards; flash memory devices including NVRAN; optical media such as CD-ROM, DVD and writeable compact disc; magneto-optical media in disc, tape or card form; paper media, such as punched cards and paper tape; or on carrier wave signal received through a network, wireless network or modem, including radio-frequency signals and infrared signals.
  • EPCR shall be understood to mean or relate to endothelial cell PC receptor.
  • Pathway shall be understood to generally mean or relate to a plurality of reactions (e.g., chemical reactions, binding reactions, and the like), such as those which are involved in biochemical, cellular, physiological, and/or pathophysiological processes.
  • a pathway may interconnect with, and/or be regulated by one or more other pathways.
  • the one or more other pathways also can be modeled as a series of chemical reactions and/or binding reactions.
  • PC shall be understood to mean or relate to protein C.
  • PS shall be understood to mean or relate to protein S.
  • UFH shall be understood to mean or relate to unfractionated heparin.
  • VTE shall be understood to mean or relate to venous thromboembolism.
  • Fig. 1 is a high level flow diagram of an exemplary method according to the present invention for assessing hemostatic risk and also for minimizing risk by determining a treatment protocol.
  • Fig. 2 is another high level flow diagram of another exemplary method according to the present invention for optimizing the treatment protocol.
  • Fig. 3 is a block diagram representing a logic sequence of a methodology for simulating in silico to assess hemostatic risk, such as described in PCT Application No. PCT/US03/07379.
  • Figs. 4A-D are block diagrams representing the logic sequence of a methodology for the solver feature of Fig. 2, such as also described in PCT Application No. PCT/US03/07379.
  • Fig. 5 is a listing of equations that are useable in the simulating methodology of Fig. 3.
  • the notation -2> signifies a forward reaction dictated by rate constant "2" or f ⁇ and the notation ⁇ 1- signifies a reverse reaction dictated by rate constant "1" or kj.
  • Fig. 6 is a tabulation of plasma composition factor levels for ACS and CAD populations and for healthy individuals.
  • Figs. 7A-D are graphical views of thrombin simulations and empirical measurements of ACS and CAD populations. Plasma compositions from 28 ACS individuals (Fig. 7A) and 25 CAD individuals (Fig. 7B) were used to generate thrombin simulations over 1200s. Computationally derived thrombin generation is shown as the mean for the ACS and CAD population (Fig. 7C). The positive standard deviation is shown in gray.
  • Empirical synthetic coagulation proteome was reconstructed on a lipid surface (PC/PS) using the purified factors (F) II, V, V ⁇ , Vm, DC, X, antithrombin (AT) and tissue factor pathway inhibitor (TFPI) (Fig. 7D).
  • the concentration of the factor levels was determined from the mean of these factors in the ACS and CAD populations. Mean physiologic concentrations were used as the control.
  • Fig. 8 is a tabulation of mean simulated thrombin parameters.
  • Figs. 9A-D are various graphical views of the normalization of the acute coronary syndrome (ACS) population.
  • factor (F) VHI, prothrombin (FIT) and antithrombin (AT) were set at mean physiologic concentration (FVIII: 0.7 nM, FII: 1.4 ⁇ M, AT: 3.4 ⁇ M).
  • FVIII 0.7 nM
  • FII 1.4 ⁇ M
  • AT 3.4 ⁇ M
  • Fig. 10 is an illustrative view of physical domain relationships.
  • Figs. 11A,B are graphical views of the dynamics of thrombin formation in contact pathway inhibited whole blood;
  • Fig. 1 IA normal versus hemophilia and
  • Fig. 1 IB thrombin thresholds for procoagulant events.
  • Fig. 12 is a graphical view of a numerical estimation of relative factor Xa production by the extrinsic factor Xase ( ⁇ ) and the intrinsic factor Xase ( ⁇ ).
  • Figs. 13 A,B are graphical views of resupply (open symbols) of Tf initiated blood coagulation reaction after the cessation of prothrombin consumption: normal (diamonds) versus hemophilia (squares); where Fig. 3 A is the numerical simulation and Fig. 13B is contact pathway suppressed whole blood.
  • Fig. 14 is an illustrative view using four cartoon panels showing a two compartment model for Tf induced blood coagulation with flow.
  • Fig. 15 is a graphical view showing simulated time courses of the formation of factor Xa, factor Va, thrombin (Eta), and meizothrombin (m-IIa) in response to a 5 pM tissue factor stimulus.
  • Fig. 16 is a graphical view of thrombin generation from contact pathway inhibited whole blood at a 20 min time point (mean ⁇ SD) in 13 individuals over a 6 month time frame.
  • the mean factor levels of the selected populations shown in Figs. 17 A,B were recapitulated in a synthetic plasma model Figs. 17C,D.
  • Fig. 18 is a graphical view of numerical simulations of thrombin generation in functionally severe hemophilia patients.
  • Figs. 19 A,B are graphical views of IVF hormone therapy influence on thrombin generation for two individuals.
  • Figs. 20A,B are graphical views of numerical simulations of the effect of factor V Leiden on thrombin generation;
  • Fig. 2OA Tf dependence of total thrombin generation normal ( ⁇ ) and factor V Leiden ( ⁇ ) and Fig. 2OB Thrombin generation with 25 pM Tf: normal ( ⁇ ), hemophilia A (D), hemophilia A plus factor V Leiden (O).
  • Fig. 21 is a graphical view of a contact pathway inhibited whole blood clot time as a function of exogenous added Tf in 2 individuals (open and filled bars).
  • Fig. 22 is an SDS-PAGE under reducing conditions of various Tf species. Note identity of placental and monocyte tissue factor.
  • Fig. 23 is a tabulation of molecular masses (Da) of TF Proteins
  • Fig. 24 is a graphical view of factor VTIa titrations of recombinant Tf (rTf) 1-263, rTf 1-242 and human placental Tf (all at O.lnM) in the absence of lipid.
  • Fig. 25 is a graphical view of thrombin generation in synthetic plasma with platelets (2xlO 8 /ml). Notice monocyte in situ and monocyte purified.
  • Fig. 26A,B are various views of Protein S (PS) preparations; Fig. 26A PS shows inhibition data and an SDS-PAGE analysis (inset) and Fig. 26B shows light scattering measurements of the rate of depolymerization of PS. Open squares: commercial PS and closed squares: in-house PS.
  • Fig. 27 is tabulation of the sets of equations describing the mechanisms of action of each of the anticoagulants used in the study described in Example 2 and which are useable in the simulating methodology of Fig. 3.
  • Fig. 28 is a tabulation of the rate constants controlling the identified bimolecular interactions and the dosing ranges recommended for prophylactic and therapeutic applications as described in connection with Example 2. .
  • Fig. 29 is a tabulation of anticoagulation in contact pathway Inhibited blood.
  • Figs. 30-A-D are various graphical views of thrombin as a function of time for showing the efficacy of AT dependent anticoagulants during the onset of Tf-initiated thrombin generation. Anticoagulants were incorporated into reactions prior to the introduction of 5 pM Tf reagent. The resulting time courses of thrombin formation are presented.
  • Fig. 3OA -UFH computational ( ⁇ ), control; ( ⁇ ), 0.015 U/mL; (•), 0.05 U/mL.
  • Predicted thrombin concentrations are given at 1 min intervals to match empirical sampling.
  • Figs. 3 IA-D are various graphical views for showing efficacy of AT independent anticoagulants during the onset of Tf-initiated thrombin generation. Anticoagulants were incorporated into reactions prior to the introduction of 5 pM Tf reagent. The resulting time courses of thrombin formation are presented.
  • Fig. 3 ID - DAPA empirical: ( ⁇ ), control; (O), 20 ⁇ M; (D), 100 ⁇ M.
  • Predicted thrombin concentrations are given at 1 min intervals to match empirical sampling.
  • Figs. 32A-D are various graphical views for showing efficacy of AT independent anticoagulants during resupply. Reactions initiated with 5 pM Tf reagent were resupplied after 20 min by the addition of an equal volume of starting material with or without anticoagulants, but no additional Tf. The resulting time courses of thrombin formation are presented. Thrombin levels for the final 5 min of the Tf-initiated episode are also shown (O).
  • Fig. 32A - UFH computational ( ⁇ ), control; (D), 0.25 U/mL; (A), 0.5 U/mL; (O), 5.9 U/mL.
  • Fig. 32A - UFH computational ( ⁇ ), control; (D), 0.25 U/mL; (A), 0.5 U/mL; (O), 5.9
  • Figs. 33A-D are various graphical views for showing efficacy of AT independent anticoagulants during resupply. Reactions initiated with 5 pM Tf reagent were resupplied after 20 min by the addition of an equal volume of starting material with or without anticoagulants, but no additional Tf. The resulting time courses of thrombin formation are presented. Thrombin levels for the final 5 min of the Tf-initiated episode are also shown (O).
  • Fig. 33 A - C921-78 computational: ( ⁇ ), control; ( ⁇ ), 1 nM; (•), 10 nM; (A), 40 nM; (O), 160 nM.
  • Fig. 33 A - C921-78 computational: ( ⁇ ), control; ( ⁇ ), 1 nM; (•), 10
  • Figs. 34 A,B are various views showing time courses of prothrombin activation after resupply.
  • Proteome mixtures initiated with 5 pM Tf reagent were resupplied after 20 min by the addition of an equal volume of starting material with or without anticoagulants.
  • Fig. 34A an immunoblot
  • control resupply no anticoagulant.
  • Lane (d) is the 20 min sample taken immediately prior to resupply.
  • Fig. 34B composite Western blot or a composite of 5 immunoblots shows the time courses of prothrombin antigen levels after resupply in the presence of each inhibitor.
  • Fig. 35 is a graphical view of TAT as a function of time for showing suppression of the resupply response in whole blood: Fpx vs. C921-78.
  • a time course of TAT formation after the addition of 5 pM Tf reagent to contact pathway inhibited blood is shown ( ⁇ ).
  • TAT levels are expressed as the total picomoles TAT versus time (min) to normalize for volume change.
  • Figs. 36 A, B are graphical view of thrombin as a function of time for showing
  • Fig. 37 is a graphical view of thrombin as a function of time showing the thrombosis history for the protein (C) population.
  • Fig. 38 is a tabulation of the evaluated population in which the model was run with and without a Protein C pathway.
  • Figs. 39A,B are graphical views of thrombin versus time of Factor VTII titration using 0 numerical simulations at 5pM TF stimulus.
  • Figs. 40 A-C are graphical views of thrombin versus time showing the influence of a 5 100-fold rise in estradiol on simulated thrombin generation.
  • Fig. 4OB shows IVF subjects at basal, after stimulation and after stimulation levels of FVHI, AT and TFPI set back to basal levels (shown as the mean and - SD in gray). 0 Fig.
  • the present invention relates to methodologies for modeling a molecular pathway and for predicting a plurality of steps in a cascade or pathway. Such modeling also can be used to predict effects of candidate compounds (e.g. , drugs) on the plurality of steps in a cascade or pathway.
  • candidate compounds e.g. , drugs
  • pathway refers to a plurality of reactions (e.g., chemical reactions, binding reactions, and the like), such as those which are involved in biochemical, cellular, physiological, and/or pathophysiological processes.
  • a pathway may interconnect with, and/or be regulated by one or more other pathways.
  • the one or more other pathways also can be modeled as a series of chemical reactions and/or binding reactions.
  • a pathway being modeled comprises "pathway components” (e.g., such as enzymes, substrates, cofactors, ligands, receptors, ions, signaling molecules, transport molecules, DNA, RNA, ribosomes, transcription factors, translation factors, and the like) and "interaction values" which are associated with the components (e.g., concentrations, rate constants, binding affinities, dissociation rates, catalysis rates, transfer rates, rates of synthesis, etc.) in a relational database according to the invention.
  • pathway components e.g., such as enzymes, substrates, cofactors, ligands, receptors, ions, signaling molecules, transport molecules, DNA, RNA, ribosomes, transcription factors, translation factors, and the like
  • interaction values which are associated with the components (e.g., concentrations, rate constants, binding affinities, dissociation rates, catalysis rates, transfer rates, rates of synthesis, etc.) in a relational database according to the invention.
  • the methodology of the present invention is embodied in a computer program product or computer program (hereinafter reference to computer program product or program product shall be understood to also include a computer program) that manipulates the values to generate a series of time-dependent concentration profiles for any/all reactants/components in a pathway over any time frame of interest. More preferably, the methodology and computer program product implements a problem solver, such as a Runge-Kutta problem solver, to perform these computations.
  • a problem solver such as a Runge-Kutta problem solver
  • the methodology embodied in the program product also can be used to model the effects of additional objects introduced into the system (e.g., compounds, such as drugs, other pathway molecules, other pathways), the effects of modifications of existing components (e.g., protein modifications, mutations, etc.), and/or the effects of altering "system values" such as temperature, pH, and the like.
  • the program product is used to model a pathway that is individualized for a particular patient, e.g., such as a patient suffering from a disease or predisposition to a disease.
  • Preferred practice involves using a methodology embodied in the program product to model the extrinsic coagulation system (particularly stoichiometric anticoagulants) and to accommodate the formation, expression, and propagation of the vitamin K dependent procoagulant complexes.
  • the model includes at least about 34 differential equations and at least about 42 rate constants.
  • the present invention provides a method for modeling the effect of a candidate compound on the pathway being modeled including the blood coagulation pathway.
  • a compound such as an inhibitor of clotting (e.g. , an antithrombotic agent) or an accelerator of clotting (e.g., a therapeutic agent for treating a hemorrhagic disease) is evaluated using the methodology of the present invention, preferably embodied in a program product, to identify particular step(s) of the pathway that would be affected by introduction of the compound.
  • the methodology and program ⁇ product embodying same is used to design an appropriate treatment strategy for a pathology which qualitatively or quantitatively alters steps of the pathway, i.e., providing a user of the program with the ability to select the parameters of a drug (e.g., range of binding affinities for a particular blood clotting factor, range of effects on catalysis, etc.) that would at least partially restore step(s) of the pathway to normal or be used as a prophylactic to reduce hemostatic risk.
  • Candidate compounds then are screened to identify those that fit selected criteria defined by the model.
  • the methodologies of the present invention including those embodied in a program product, model one or more pathways that is/are individualized for a particular patient.
  • patient-specific concentrations of pro- and anti-coagulants are inputted into the computer program model such as through a user interface.
  • the computer program model generates time-dependent concentration profiles for reactants unique to that patient over a time frame of interest (e.g., during a period when symptoms are expressed or during a period when symptoms are not expressed or when the patient is exposed to a particular therapy).
  • the patient has a congenital or acquired condition which affects blood clotting or the model is utilized to predict or determine if the patient or subject is pre-disposed to for example, thrombosis.
  • Such conditions include, but are not limited to, deficiencies in one or more of fibrinogen, Factors ⁇ , V, VH, VHI, DC, X, XI, and XH, well deficiencies in ATm, plasminogen, protein C, protein S, etc; and conditions caused by exposure to agents such as heparin, Coumadin, etc. Changes in rate constants themselves also can be modeled by empirically determining rate constants unique to a patient using methods routine in the art.
  • the methodology is structured so that a limited array of coagulation factors, all of which are at levels considered clinically normal, can be integrated by a computational model to predict an individual's potential to generate thrombin and thus be related to that individual's hemostatic risk in a variety of settings.
  • acquired data indicates that hypothetical thrombin generation based upon coagulation factor composition can distinguish between ACS and CAD.
  • Individuals within the ACS population generated significantly higher levels of thrombin at a 50% faster rate resulting in more thrombin over the time course of the TF-initiated coagulation reaction.
  • prothrombotic thrombin generation profiles of individuals with ACS appear to be driven primarily by collective alterations in factor VTQ, antithrombin and prothrombin levels. These results suggest that a limited array of coagulation factor and inhibitor levels, all largely within the accepted normal range for these factors/levels, potentially contribute to the procoagulant phenotype in acute phases of CAD. This supports the concept that
  • TF tissue factor
  • the phenotypic blood composition at any time not only reflects ongoing systemic events (e.g. inflammation) but also genetic predisposition.
  • blood composition responds relatively rapidly to external influences compared to the overall progression of the vascular remodeling.
  • these changes in blood composition can translate into either a more or less procoagulant state, which can be evaluated by methods which assess thrombin generation.
  • the procoagulant phenotype observed in the ACS population derived from relatively small changes (between 10 and 30%) primarily in only three factors, and is either a consequence of the acute event (that is developed within the first 12 hours from the onset of chest pain) or predated the event by some time interval.
  • the procoagulant phenotype appears to depend primarily on the influence of AT, factor VIH and prothrombin upon thrombin generation. In general, alterations in the production or clearance rates of these factors could yield these changes. It should be noted that since two of the factors are elevated and one decreased, the alterations are not uniform.
  • the integration of blood composition data into an assessment of thrombin generation potential discriminates between acute and stable CAD and also that a limited array of factors can be predictive. It should be recognized that while the foregoing examples refer to ACS and CAD, the present invention is not limited to applying the modeling methodology for such applications.
  • modeling methodologies include, but not limited to, the following areas of applicability are: hemophilia (A (fVTfl), B (fix), C (factor XI), factor VII and V deficiency), thrombophilia factor V Leiden and thrombosis Protein C deficiency, Antithrombin deficiency hyperprothrombinemia genetic mutations that effect analytes, In vitro fertilization, oral contraceptive use, genetic mutations that effect analyte levels, fibrinogenemias platelet disorders, Coumadin therapy, anticoagulant therapy (unfractionated heparins, synthetic heparins, direct thrombin inhibitors, direct factor Xa inhibitors, factor Vila inhibitors, factor IXa inhibitors, surgical procedures (pre and post operative) trauma, or resuscitative therapy coagulopathies associated with traumas.
  • the modeling methods of the present invention are useable for drug development as the modeling methodology output(s) is inclusive of
  • thrombin- AT-IH TAT
  • Fig. 1 IA TAT and active thrombin
  • nM small amounts of thrombin are produced in an interval, which is defined operationally as the INITIATION PHASE of the reaction. Subsequently, the major bolus (>96%) of thrombin is produced during a PROPAGATION PHASE.
  • thrombin activates the substrates required to provide the catalysts (Fig. 1 IB), which generate the majority of the thrombin produced during the PROPAGATION PHASE of the reaction.
  • the rate- limiting component of prothrombinase complex formation and the ultimate generation of thrombin activity is the concentration of fXa.
  • the activation of platelets and fV occur rapidly to produce surplus fVa and platelet membrane binding sites (Fig. 1 IB).
  • the Tf initiated reaction can become sensitive to fV or platelets.
  • 11 A,B depends on the generation of ⁇ 2nM active thrombin.
  • Tf thromboplastin
  • phospholipid a concentration of 5pM Tf was used, producing a clotting time of ⁇ 5 min.
  • the clotting time is somewhat prolonged, however, the major defect is associated with the absence of a PROPAGATION PHASE (Fig. 1 IA).
  • the vascular endothelium in healthy individuals, provides an anticoagulant blood container, which is both stoichiometric and dynamic in its opposition to thrombin generation.
  • the vascular surface itself can support a prothrombotic response.
  • Extravascular tissue surrounding the endothelial cell layer also provides connective tissue collagens, which interact with von Willebrand factor and platelets. This tissue is also a rich source of Tf.
  • the third major contributor to clot formation is an altered flow state.
  • flow and/or stasis within the blood vessel supplies additional contributions to processes occurring at the site of a vascular lesion.
  • risk factors for venous thrombosis including PC, PS and AT-HI deficiencies; fy Leidei ⁇ prothrombin 20210 gene mutation, high fibrinogen and JFvTfI, etc.
  • heterozygous presentation or elevated levels of any of these components is not a signal for clinical intervention with an anticoagulant.
  • Pharmacologic interventions that are used in VTE are principally Coumadin and heparinoids in various forms ranging from synthetic pentasaccharides through fragmented heparinoids to conventional heparin.
  • Anticoagulants on the horizon include protease inhibitors for fXa and fIXa.
  • the thrombin inhibitor Hirulog (Angiomax) has seen increasing success in invasive cardiology.
  • the oral thrombin inhibitor Ximelagatran looked promising but has not received FDA approval because of liver toxicity.
  • chronic interventions with aspirin and clopridogrel are common while both ReoPro and Integrilin have found roles in invasive cardiology.
  • Statins may also play roles in suppressing thrombin generation.
  • the numerical modeling embodied in the present invention resulted from the realization that analysis of the kinetics of the formation and expression of this 5 component system (fXa, fVa, fl, phospholipid, Ca++), could not be accomplished using the traditional biochemical approaches of changing one variable at a time. Therefore, the numerical model embodied in the present invention was developed so as to embody the stoichiometry and enzymology of prothrombinase formation and catalytic function. Using this model, conditions were def ⁇ nded under which paradoxical situations might be observed such as inhibition by excess substrate, enzyme or membrane. With the aid of this computer model, conditions can be defined under which empirical laboratory tests can be performed to illustrate this paradoxical behavior. Empirical conformity to theory encouraged further modeling.
  • concentrations of the procoagulant factors (II, V, VII, Vila, VHI, DC, X) and the anticoagulants (ATIH, TFPI) that are "exposed" to pM concentrations of Tf in the model can reflect mean physiological concentrations for each factor or actual values for an individual. Simulations can be generated in which rate constants are varied or in which competing or novel pathways are incorporated. Entering new reaction pathways does not require writing differential equations; the software program embodying the methodology of the present invention, based upon chemical notation, automatically generates the equations.
  • Fig. 13A illustrates a computer model of hypothetical additions of blood to a reaction system, which, after Tf activation, achieved a quiescent level of thrombin formation (plateau of TAT).
  • TAT thrombin generation
  • FIG. 13B illustrates an empirical study in which corn trypsin inhibitor (CTI) whole blood activated by the addition of Tf is allowed to become quiescent with respect to thrombin generation (TAT plateau see also Fig. 1 IA).
  • CTI corn trypsin inhibitor
  • Fig. 14 The reaction/flow control of bleeding is illustrated in Fig. 14, which hypothesizes a two compartment model for the regulation of Tf induced blood coagulation with flow. This model is illustrated in four cartoon panels.
  • Perforation results in platelet adhesion, vascular Tf presentation and formation of an imtial platelet-fibrin plug (Fig. 14-2). Additional reactants supplied under flow (Fig. 14-3) eventually plug the vascular defect. Active components still present in the plug provide uninhibited fTXa-fVIIIa and fXa-fVa which will rapidly initiate more thrombin-plug formation if exit flow continues.
  • Downstream (Fig. 14-4), enzymes and cofactors escaping from the plug perforation reaction are captured by AT-rH-heparan-proteoglycan complexes while cofactors are inactivated by the PC system.
  • Fig. HB Early events of Tf initiated coagulation (Fig. HB) involve the generation of the catalysts and cofactors, which then assemble into the pro-coagulant complexes responsible for most thrombin generation. These amplifying cycles take place at levels of thrombin frequently at the subnanomolar range. Femtomolar to picomolar enzyme concentrations lead to the generation of complex catalysts that are 10 5 -10 7 times more efficient than the naked enzymes that carried out the initial catalytic events.
  • the computer models describing these early events were instrumental in designing experiments and verifying conclusions because of the low concentrations of reactants and products. A display of these hypothetical early events predicted by one of these computer models is illustrated in Fig. 15.
  • thrombin generation approaches the status of a phenotypic property, consistent with empirical reconstructions and computer models.
  • Venous thrombosis is most frequently associated with the accumulation of genetic- environmental alterations resulting in incomplete penetrance of any one polymorphism.
  • hemophilia A and hemophilia B are monogenic hemorrhagic defects clearly identified with risk with therapy available.
  • hemophilia A a variety of phenotypic expressions are seen.
  • Dr. van den Berg of the van Creveldklinek Dutch National Hemophilia Treatment Center in Utrecht, N.L. 41
  • individuals with severe hemophilia A were evaluated. Based upon composition data, the hypothetical ability of these individuals to generate thrombin was calculated. The thrombin generation hypothetical constructs for 5 individuals at each extreme are shown in Fig. 18.
  • Figs. 19 A,B graphical views for two individuals, one of whom shows little or no increased hypothetical thrombin generation following hormonal treatment (Fig. 19A) while the other shows massive increases in thrombin generation reflecting alterations in coagulation factor composition (Fig. 19B). It is inferred that the latter individual would be more susceptible to a thrombotic event in this therapeutic protocol.
  • fVIIa recombinant fVIIa
  • This deficiency state while rare in the general population (1 in 1 million), is more common in Quebec (heterozygous fVTI deficiency 1 in 550). Since a natural or recombinant fVTI therapeutic product was not available, the efficacy of utilizing rfVTfa using our model systems was evaluated. This study is important because fVII binds Tf and thus buffers fv ⁇ a. Thus, in a fVII CRM (-) individual, the rfVIIa levels needed would be significantly lower than those required for an individual with hemophilia A. The numerical simulations supported this hypothesis and these data were largely replicated in proteome models and by reconstitution of fVTI (-) blood in vitro and in vivo in fVII deficient individuals.
  • the model and methodology were refined so that this includes elements of thrombin-Tm activation of PC and APC inactivation of fVa by virtue of cleavage at 306 and 506 in the peptide structure.
  • Studies of the inactivation process of fVa supported by R01-HL34505 provides the mechanistic/quantitative data for this reaction and a global kinetic model for the loss of activity in the function of fVa following APC cleavage.
  • Applications of the model fVa inactivation process are illustrated in Figs. 20A,B. Fig.
  • FIG. 2OA reflects the amount of thrombin which would be generated in homozygous fy Leiden plasma as compared to normal as a function of Tf and is consistent with the pathology of this mutation, hi Fig. 2OB, an illustration of a hypothetical fVIII deficiency with coinheritance of fV Leiden , a situation which has been hypothesized to be protective from hemophilia, is compared to normal and hemophilia with normal fV.
  • the observations of Fig. 2OB led to the development a collection of inhibitors of APC. These compounds yield increases in thrombin generation similar to those observed in Fig. 20.
  • Tf also was isolated from THP-I monocytes after stimulation with LPS.
  • a compilation of the molecular weights of each Tf is shown in the tabulation provided in Fig. 23 This tabulation displays the only report of fundamentally sound molecular weight analyses for Tf preparations based on MALDI-TOF mass spectrometry.
  • Tf proteins are distinguished by functional activities in simple as well as in complex reaction systems (Figs. 24, 25). Scatchard analysis shows that each Tf protein forms an equimolar complex with fV ⁇ a. This 1 : 1 stoichiometry (based on the true molecular weights) validates assays used for the evaluation of Tf concentration and indicates that the observed difference in functional activity is not caused by erroneous estimates of Tf proteins.
  • monocyte Tf In complex, placental Tf displays a lower K ⁇ and a higher V ma ⁇ than the recombinant proteins (Fig. 24).
  • Monocyte Tf in situ quantitated by immuno-chemistry, expresses significantly higher activity than monocyte Tf purified and reconstituted on phospholipids; purified monocyte Tf at 5pM displays similar activity to monocyte Tf in situ at 0.05pM.
  • monocyte Tf In the reconstituted coagulation platelet proteome, monocyte Tf displays ⁇ 400x the activity associated with monocyte Tf reconstituted on synthetic membranes.
  • Fig. 26A shows clot inhibition data for purified recalcified PS and normal plasma added to PS deficient plasma. Dilutions of a normal plasma pool fall on this curve (open symbols). This titration indicates that normal plasma contains ⁇ 40nM free PS. The convergence of plasma dilution data with the purified protein curve lends support of the preservation of natural PS quality in the material we isolate. The rate of depolymerization of Ca " " " free PS upon addition of Ca + * using light scattering (Fig. 26B) was evaluated. At 25°C, the half time for this reaction is approximately 100s, thus the PS effect is significantly diminished in assays initiated in vitro in citrate plasma by the addition of
  • Fig. 1 a high level flow diagram illustrating a method according to the present invention for assessing hemostatic risk and also for minimizing risk by determining a treatment protocol.
  • the process starts at Step 100 and thereafter the logic flow then continues to Step 110.
  • a clinician or technician acquires a biologic sample from the subject or patient that is appropriate for use in determining the parameters necessary for the simulation in silico.
  • the clinician or technician or other medical personnel withdraws one or more samples of blood from the patient or subject.
  • Step 114 the logic flow continues to Step 114, where the subject's analytes are determined. Such determining includes measuring and quantifying each of the analytes. Thereafter, the logic flow continues to Step 104 in which a simulation of a biological process, for example a blood coagulation process, is performed such as described further herein. The simulation yields one or more outputs of parameters, for evaluation and analysis by the clinician or other medical personnel, Step 106.
  • a biological process for example a blood coagulation process
  • the clinician compares the outputs to reference values or other criterion for determining if the outputs are symptomatic of a disease state or that the results indicate that the patient or subject is pre-disposed such that an undesirable medical event could occur in the future. For example, the results could indicate that the subject is likely susceptible to the occurrence of a clotting event or other hemostatic risk. If the clinician determines that treatment is not required or suggested (No, Step 118), then the evaluation process is concluded, Step 120. !
  • the clinician determines that the results or outputs are indicative of a need to initiate treatment so as to either treat a disease condition or to initiate a prophylactic treatment to reduce the risk to the patient or subject to minimize the hemostatic risk to the patient (Yes, Step 118), then the clinician would determine an appropriate treatment protocol, Step 130.
  • the results could indicate that the subject appears susceptible to undesirable blood clotting and thus the clinician could suggest a treatment protocol to be followed to minimize the risk of such clotting occurring in the future.
  • Step 132 the patient or subject would be treated, Step 132, and such treatment would be monitored by the clinician, Step 134, in any of a number of ways know to those skilled in the art and appropriate for the particular treatment protocol.
  • the clinician also would evaluate the efficacy of the treatment protocol from time to time to determine if the protocol is effective or if other protocols should be considered, Step 136.
  • Step 136 If the clinician concludes that the treatment protocol is effective (No, Step 136), then the monitoring of the treatment protocol is continued, step 134. Thereafter process steps 134-136 are repeated until the clinician determines that the treatment protocol is not or no longer effective (or the treatment protocol is otherwise terminated). In the case where the clinician concludes that the treatment protocol is not or no longer effective (Yes, Step 136) then the process steps 110-132 are repeated to develop another treatment protocol.
  • the simulation methodology is utilized to optimize or arrive at a treatment protocol for the patient or subject. Now referring to Fig. 2 there is shown a high level flow diagram of such a process. It should be recognized that this process carries out the functionality contained in Fig. 1, Step 130.
  • the clinician would identify an treatment therapy which would appear to be appropriate for the subject, step 120.
  • a treatment therapy would include the intensity of the therapy.
  • this also would include the number of doses and dosage of such drugs, medicaments, replacement therapy or the like.
  • a' simulation is run with the treatment therapy as an input to the simulation process, Step 122.
  • This simulation would be similar to that described herein such as for example, step 104 of Fig. 1.
  • the clinician evaluates the output to determine if the treatment therapy or treatment protocol is effective, Step 126. If the treatment plan or protocol is determined that to be effective (Yes, Step 126), then the process would return to Fig. 1, Step 132.
  • step 120 the clinician would alter the treatment therapy, for example increase or decrease the dosage of the drug being administered and then repeat steps 122-126 of the process until a treatment therapy plan/protocol is arrived at. It should be recognized, that it also is within the scope of the present invention for a clinician to use this process to evaluate a number of possible different treatment therapies to determine which of the possible different treatment therapies is optimal.
  • the present invention is particularly advantageous, because the inputs to the simulation (based on the obtained biologic sample) are patient specific and thus the evaluation is not based solely on an assessment of the results in view of their normalcy to demographic or population range of values for healthy individuals. In contrast, most decisions using prior art techniques are usually based on an evaluation of a parameter with respect to the normal range of values for healthy individuals. A problem is assumed to exist when the parameter is out of the normal range.
  • the present invention also is particularly advantageous because the simulation provides a mechanism by which the clinician can assess a subject's hemostatic risk before there is threat of the risk related event occurring.
  • the present invention also is particularly advantageous, as it allows the clinician to simulate the treatment plan/protocol and assess its effectiveness before the subject or patient is exposed to the treatment plan.
  • the simulation methodology of the present invention can be used to assess the effectiveness of a given drug having certain characteristics or qualities to treat a given condition
  • the simulation methodology also is adaptable for use in drug development. Simply, one can input qualities related to a drug and target a given pathway for example. Thereafter, one assesses the effectiveness of a drug having that quality following such simulation. In the case where it is determined from the simulation that those drug qualities would be effective, then one can evaluate the pharmaceutical databases to identify a compound that exhibits the identified qualities. Thereafter, the efforts normal required to bring a drug to market can then be undertaken.
  • the flow charts herein illustrate the structure or the logic of the present invention as embodied in computer program software for execution on a computer, digital processor or microprocessor.
  • Those skilled in the art will appreciate that the flow charts illustrate the structures of the computer program code elements, including logic circuits on an integrated circuit, that function according to the present invention.
  • the present invention can be practiced by a machine component that renders the program code elements in a form that instructs a digital processing apparatus (e.g., computer, digital signal processor, AISC or the like) to perform a sequence of function step(s) corresponding to those shown in the flow diagrams.
  • a digital processing apparatus e.g., computer, digital signal processor, AISC or the like
  • an application programs embodying the methodology of the present invention shall include code segments, instructions and criteria including data as well as audio and visual data, so the applications program can carry out the below described functions/ methodology.
  • Fig. 3 there is shown a block diagram representing a logic sequence of a methodology for simulating in silico so as to thereby assess hemostatic risk. Beginning at Step 200, a user logs in with a user name and password. Logic flow then continues at Step 202, where the user may select existing equations from a prior run of the program product. An exemplary listing of such equations is provided in Fig.
  • Step 204 where the methodology embodied in the program creates new IDs for the species and rate constants.
  • Logic flow then continues at Step 206. If the user does not select the existing equations (No, Step 202), then logic flow continues at Step 206, where the user inserts new equations into the database. Logic flow then continues at Step 208. In Step 208 a feature called solver (e.g., a subroutine, called the "solver”) breaks down the equations to individual species and rate constants.
  • Step 210 where the user selects new or old species. If the user selects the new species (Yes, Step 210), the logic flow continues at Step 212, where the new species is inserted into the database. Logic flow then continues at Step 214. If the user does not select new species but instead selects the old species (No, Step 210), the logic flow bypasses Step 212, and continues at Step 214.
  • solver e.g., a subroutine, called the "solver”
  • Step 214 the user selects new or old rate constants. If the user selects new rate constants (Yes, Step 214), logic flow continues at Step 219, where new rate constants are inserted into the database. The logic flow then continues at Step 220. If the user does not select new rate constants but instead selects the old rate constants (No, Step 214), the logic flow bypasses Step 219 and continues at Step 220.
  • Step 220 all data used for the calculations is stored in a text file.
  • the solver parses the text file created in Step 220, creates the corresponding equations, and solves them.
  • Step 224 where the results of the calculations are saved to the database.
  • Step 226 where the user selects whether to display the data on the monitor as graphical data. If the user does not wish to display the data on the monitor (No, Step 226), the logic flow continues at Step 228, where the data is output to an Excel® formated file. If the user wishes to display the data on the monitor as a graph (Yes, Step 226), the logic flow continues at Step 230, where the data is displayed. After display or output in Steps 230, 228, respectively, the logic flow terminates at Step 240.
  • Figs. 4A-D there are shown block diagrams representing the logic sequence of a methodology for the solver feature step of Fig. 3.
  • the solver step of Fig. 3 is detailed more fully in the flowcharts of Figs. 4 to 4D, and is the result of a mathematical model that was created to aid in the understanding of the blood coagulation system by modeling the kinetics of the enzyme linked systems.
  • the solver step is limited to second order reactions, since its primary design is for use in biochemistry models.
  • the methodology embodied in the software utilizes the format of equations (1-2) to generate expressions (3-6), thus eliminating human error often introduced when preparing these expressions by hand.
  • the software prompts the user for initial concentrations and rate constant values. These values are then used to model the linked system and generate anticipated time dependent concentration values for each species at user specified intervals.
  • the software performs rapidly, and, in one practical embodiment, generated time dependent concentration values for a system utilizing twenty-five (25) species tracked for five (5) minutes at one (1) second intervals in approximately fifteen (15) seconds.
  • equation (10) is the result of explicit differentiation of the rate equation (7), yielding a simple exponential problem. Due to the nonlinear character of expressions (3-6), no such explicit solution is available. It will be appreciated that an infinite Taylor series expansion could have been utilized to indicate solutions to the differential expressions (3-6), and that computational algorithms based upon the Taylor series could have been utilized to describe the next step of a function y through its course: dy , ⁇ x d 2 ⁇ x 2 d 3 , ⁇ x 3 dx 1 ! dx 2! dx 3!
  • Hindmarsh's LSODE was based on Backward Differentiation Formula (BDF) methods, mostly using 3rd order polynomials, but took control of the step size, and thus results in a more efficient computation.
  • BDF Backward Differentiation Formula
  • Runge-Kutta method utilizes defined step intervals (in kinetics, this interval was time) to approach an experimental solution, the success of this method is largely dependent upon a careful choice of stepsize.
  • the stepsize value should be optimized appropriately in order to maximize computational utility and experimental value.
  • the use of large (greater than one (1) second) intervals was found to result in computational instability, resulting in the return of zero or NaN (not a number, i. e. , infinite) values for the concentration of a few or most species of interest.
  • Routine use of 0.01 second intervals resulted in computational stability for the coagulation simulations, and yielded values identical to similar type solvers running on diverse computer systems with diverse compilers, such as RlOK Silicon Graphics Inc.
  • the software program calculates the species concentrations every tenth (0.1) second. Thus, a twenty (20) minute simulation generates 12,000 data points for each species. In the case of twenty (20) species, this results in 240,000 data points. Therefore, by default, the software generated data points at one (1) second intervals. It will be further appreciated that no round off error was introduced, as this method simply defined the data points to be output.
  • the modeling embodied in the software program defines a rigorous system of inputting chemical equations in a manner that allows the computer to model the relationships among reactants, and products with their rate constants.
  • the program also is designed such that reversible equilibrium expressions could be input as a single line, i. e. , there was no need independently to define the forward and reverse reactions using separate lines.
  • limits imposed upon the scripting language include: 1. Species names are limited to 20 characters in length and can contain any character string with the exception of the +, >, ⁇ , and - characters without spaces.
  • the equation parser is case sensitive, i.e., a distinction is made between capital letters and lowercase letters. The characters >, ⁇ , and - are reserved to denote the direction of reaction progress and rate constant identity. 2.
  • the total number of unique species in any system is limited to 1000.
  • the total number of rate constants is limited to 1000.
  • rate constants are numeric integers of value less than 100. This is for the rate constant description, and not its value. 5. The maximum number of species that can be included in one side, e.g. , on the
  • RHS of a single expression
  • Step 300 the logic sequence for the solver step begins at step 300, and proceeds to a decision step, Step 302, where the user is interrogated as to whether to use another user's equations. If the user wishes to use another user's equations (Yes, Step 302), the logic flow continues at Step 304, where the software program fetches a file containing the equations from the database, where they were saved from a previous run.
  • Step 304 If the user does not wish to use another's equations (No, Step 304), the logic flow continues at Step 306, where the user selects from the equations to be used for a run. At Step 308, the software program fetches the file from the database with the selected equations. The logic flow then continues at Step 310, or "B".
  • Step 320 the software program interrogates the user as to whether the user wishes to modify the selected equations. If the user wishes to modify the selected equations (Yes, Step 320) the logic flow continues at Step 322, where the user inputs the modified equations. The logic flow then continues at Step 324. If the user does not wish to modify the equations (No, Step 320), the logic flow continues at Step 324.
  • the software program interrogates the user as to whether the user wishes to user old species. If the user wishes to use old species (Yes, Step 324), the logic flow ; continues at step 326, where the program fetches the old species from the database. The logic flow then continues after Step 328. If the user wishes to use new species from Step 324 (No), the logic flow continues at Step 328, where the new species are used.
  • the solver parses the equations into species and rate constants. The logic flow then continues at Step 342, where the program creates an output file with the total number of species, a list of all species, with one species on each line, the total number of rate constants, and a list of rate constants, with one rate constant on each line. The logic flow then continues at Step 350, or "C".
  • Step 360 the software program interrogates the user as to whether the user wants to modify the species concentrations. If the user wishes to modify the species concentrations (Yes, Step 360), the logic flow continues at Step 362. Thereafter or in the case the user does not wish to modify the species concentrations (No, Step 360), the logic flow continues at Step 364.
  • Step 364 the software program interrogates the user as to whether the user wishes to modify the rate constants. If the user wishes to modify the rate constants (Yes, Step 364), the logic flow continues at Step 366, where the user inputs the rate constants. Thereafter or in the case the user does not wish to modify the rate constants (No, Step 364), the logic flow continues at Step 368.
  • the software program interrogates the user as to whether the user wishes to select the duration, to identify the species to be output, or to modify the stepsize. If the user desires to select the duration, to identify the species to be output, or to modify the stepsize (Yes, Step 368), the logic flow continues at Step 370, where the user inputs the duration, the species to be identified on output, and the modified stepsize. The logic flow then continues at Step 372. If the user chooses not to make any selections at Step 368, the logic flow continues at Step 372, where the software program interrogates the user as to whether a titration is desired.
  • Step 372 If the user desires a titration (Yes, Step 372), the logic flow continues at Step 374, where the user selects the rate constants or species for the titration. The user also selects modify, high/low (150%, 100%, 50% of value entered previously) or user defined for the titration. The logic flow then continues at Step 375, or "D". If the user does not select titration (No, Step 372), the logic flow continues at Step 375.
  • Step 380 the solver parses the equations into the species and rate constants.
  • the logic flow then continues at Step 382, where the program compiles the dC/dt expressions.
  • Step 384 the software program solves the dC/dt expressions using the Runge-Kutta method as described herein and in US. Patent Publication US 2006/0015261.
  • the software program is then caused to output, at Step 386, a file with the selected species concentrations at the selected interval until the selected duration is completed.
  • Step 370 it is determined if the user selected a titration.
  • Step 370 If the user selected a titration (Yes, Step 370), the logic flow returns to Step 384 and Steps 384 and 386 are repeated until the titration is completed. If no titration was selected (No, Step 370), the logic flow ends at Step 372.
  • Coronary artery disease is the leading cause of death for both men and women in the United States each year. Atherosclerotic lesions within the coronary arterial system are subject to mechanical and hemodynamic forces that can trigger disruption of atherosclerotic plaques [Lee RT, Kamm RD. Vascular mechanics for the cardiologist. J Am Coll Cardiol 1994; 23: 1289-95; Maclsaac Al, Thomas JD, Topol EJ. Toward the quiescent coronary plaque. J Am Coll Cardiol 1993; 22: 1228-41]. Thrombus formation after plaque rupture is the trigger for abrupt coronary artery occlusion and subsequent acute coronary syndrome (ACS) [FaIk E, Shah PK, Fuster V. Coronary plaque disruption. Circulation 1995; 92: 657-71].
  • ACS acute coronary syndrome
  • Circulating factor Vila (FVIIa) combines with the newly exposed TF and activates the zymogens FIX and FX to their respective serine protease, FIXa and FXa (for review see [Mann KG, Brummel K, Butenas S. What is all that thrombin/or? J Thromb Haemost 2003; 1: 1504-14]).
  • the procoagulant intrinsic and prothrombinase complexes are subsequently formed and ultimately result in prothrombin activation to thrombin.
  • Thrombin catalyzes the transformation of soluble fibrinogen molecules to fibrin, potently activates platelets, and amplifies its own generation by activating the plasma procofactors FV and FVTH and the zymogen FXI.
  • Thrombin's procoagulant activity is regulated by dynamic (protein C) and stoichiometric [antithrombin (AT) and tissue factor pathway inhibitor (TFPI)] inhibitory pathways.
  • Prothrombin fragment 1 + 2 present at 1-2 nM, is roughly 2- fold higher than observed in stable angina patients, reaching maximum values in subjects displaying both ST segment elevation myocardial infarction (STEM I) and markedly elevated cardiac troponin, a marker of myocardial necrosis [Hoffmeister HM, Ehlers R, Buttcher E, Steinmetz A, Kazmaier S. Helber U, Szabo S, Beyer ME, Seipel L. Relationship between minor myocardial damage and inflammatory acute-phase reaction in acute coronary syndromes.
  • STEM I ST segment elevation myocardial infarction
  • cardiac troponin a marker of myocardial necrosis
  • Elevated fibrinopeptide A another indicator of thrombin activity during the early phase of myocardial infarction, has been shown to be an independent predictor of cardiac mortality [Li YH, Teng JK, Tsai WC, Tsai LM, Lin LJ, Guo HR, Chen JH. Prognostic significance of elevated hemostatic markers in patients with acute myocardial infarction. J Am Coll Cardiol 1999; 33: 1543-8].
  • Thrombin generation phenotypic quantitation. J Thromh Haemost 2004; 2: 281-8; Brummel-Ziedins K, Whelihan ME, Ziedins EG, Mann KG. The resuscitative fluid you choose may potentiate bleeding. J Trauma 2006; 61: 1350-8].
  • the plasma hemostatic proteome thrombin generation in healthy individuals. J Thromb Haemost 2005; 3: 1472-81; Carr ME Jr, Martin EJ, Carr SL. Delayed, reduced or inhibited thrombin production reduces platelet contractile force and results in weaker clot formation. Blood Coagul Fibrinolysis 2002; 13: 193-7; Hartert HS. The phsical and biological constants of thrombelastography. Biorheology 1962; 1: 31-9; Hron G, Kollars M, Binder BR, Eichinger S, Kyrle PA. Identification of patients at low risk for recurrent venous thromboembolism by measuring thrombin generation. JAMA 2006; 296: 397-402].
  • Vaziri et al. [Vaziri ND, Kennedy SC, Kennedy D, Gonzales E. Coagulation, fibrinolytic, and inhibitory proteins in acute myocardial infarction and angina pectoris. Am J Med 1992; 93 : 651-7] reported increased FIX and decreased FU and FV in ACS patients. Elevated FVDI, which predisposes to venous and arterial thrombosis [Martinelli I. von Willebrand factor and factor Vi ⁇ as risk factors for arterial and venous thrombosis.
  • HEPES and ethylenediaminetetraacetic acid (EDTA) were purchased from Sigma Chemical Co. (St Louis, MO, USA).
  • Human coagulation FVTI, FX, FIX, and prothrombin were isolated from fresh frozen plasma using the methods of Bajaj et ah, and were purged of trace contaminants and traces of active enzymes.
  • Human FV and AT were isolated from freshly frozen plasma [Katzmann JA, Nesheim ME, Hibbard LS, Mann KG. Isolation of functional human coagulation factor V by using a hybridoma antibody. Proc Natl Acad Sci USA 1981 ; 78 : 162-6; Griffith MJ, Noyes CM, Church FC. Reactive site peptide structural similarity between heparin cofactor 11 and antithrombin 111. J Biol Chem 1985; 260: 2218-25].
  • Recombinant FVHI and recombinant TF were provided as gifts from Drs Shu Len Liu and Roger Lundblad (Hyland division, Baxter Healthcare Corp, Duarte, CA, USA).
  • Recombinant human FVTIa was provided as a gift from Dr UIa Hedner (Novo Nordisk, Denmark).
  • PCPS Phospholipid vesicles
  • Exclusion criteria in ACS patients were as follows: cardiogenic shock, any acute illness, cancer, hepatic or renal dysfunction, history of venous thromboembolism or stroke, oral anticoagulant or heparin administration, previous coronary artery bypass grafting surgery.
  • Myocardial infarction was defined according to the ESC/ ACC criteria [Myocardial infarction redefined - a consensus document of The Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction. Ear Heart J 2000; 21 : 1502-13]. All patients took aspirin 300 mg 6 to 12 h before the study. None of the subjects received thienopyridines prior to blood collection.
  • Blood collection and coagulation protein analyses Blood was drawn into 0.1 volume of 3.2% trisodium citrate from an antecubital vein with minimal stasis (within 15 min upon admission in case of ACS patients). Citrated blood samples were centrifuged (3000 x g for 20 min) within 15 min of collection and stored in aliquots at -80 0 C until further use. Lipid profiles, blood cell counts, glucose, creatinine, aminotransferases, cardiac troponin I (cTnl) and MB creatine kinase were assayed by routine laboratory techniques. There were no differences in glucose levels between both groups as well as in the percentage of patients with diabetes. Fibrinogen was determined using the Clauss method.
  • Total TFPI was determined by ELISA (Diagnostica Stago, Asnieres, France). FIT, FV, FVII, FVTH, FDC, FX were measured by one-stage clotting assays using factor-deficient plasmas (Dade Behring, Liederbach, Germany). AT activity was measured using Berichrom (Dade Behring).
  • Factor V 40 ⁇ M
  • FVHI 1. nM
  • Prothrombin (2.8 ⁇ M), FVII (20 nM), FVHa (0.2 nM), FX (340 nM), FIX (180 nM), FXI (60 nM), TFPI (5 rim), and AT (6.8 ⁇ M) are preheated in HBS, 2 mm CaCl 2 , at 37 0 C for 3 min.
  • the reaction is started by mixing equal volumes of both Ca 2+ pre-equilibrated solutions resulting in physiological concentrations of the zymogens, pro-cofactors and inhibitors, 5 pM TF, 2 mm CaCl 2 and 2 ⁇ M PCPS.
  • the mean blood coagulation factor levels of both the ACS and CAD populations were within the reference values for healthy individuals (See tabulation provided in Fig. 6).
  • the concentrations of TFPI, AT, FlI and FVIH differed significantly (P ⁇ 0.01 ) between the groups, with levels of FIT, FVHI and TFPI higher and AT lower in ACS patients.
  • Figs. 7A-D present the thrombin generation curves for each individual within the ACS (Fig. 7A) and the CAD (Fig. 7B) populations.
  • An analysis was conducted in which all the thrombin generation curves within each population were averaged by combining ⁇ thrombin second by second over the time course of 1200 s.
  • the resulting mean thrombin concentration over time simulation and the positive standard deviation is presented for each group in Fig. 7C.
  • the simulated thrombin parameters are presented in the tabulation provided in Fig. 8 as the mean ⁇ SD and range for the ACS and CAD populations and compared with a control reflecting mean physiologic concentrations. Significantly higher maximum levels, rates and total thrombin are seen when the ACS population is compared with the CAD population (P ⁇ 0.001).
  • Fig. 7D The mean factor concentrations of the ACS and CAD populations (See tabulation in Fig. 6) were used in empirical coagulation proteome experiments with a 5 pM TF stimulus. These results are shown in Fig. 7D.
  • Fig. 7C By recapitulating the mean procoagulant and anticoagulant protein profiles of the ACS and CAD populations in the presence of a phospholipid surface and TF, we obtain thrombin generation profiles for the populations that mimic the pattern observed in the numerical simulation in Fig. 7C. Normalization of thrombin generation
  • the goal was to identify those protein(s) concentrations which might account for the difference in thrombin generation profiles between the ACS and CAD populations vs.
  • Thrombin generation profiles were initially computed by successively holding each protein (eight total) at their mean physiologic value while the other seven were used at their mean ACS population value.
  • Fig. 9D CAD level
  • prothrombotic thrombin generation profiles of individuals with ACS appear to be driven primarily by collective alterations in FVHI, AT and FE levels. These results suggest that a specific array of coagulation factor and inhibitor levels, all largely within the normal range, can potentially contribute to the procoagulant phenotype in acute phases of CAD. This supports the concept that 'vulnerable' circulating blood with prothrombotic alterations and a vulnerable plaque, a potential source of TF, may both play a role in the development of ACS.
  • Therapeutic agents that regulate blood coagulation are critical to the management of thrombotic disorders and surgical interventions. Many new compounds that were plausible in terms of target, relative specificity, mechanism and kinetic and thermodynamic properties have failed to perform as expected when assessed, at significant expense, in vivo.
  • the mathematical model and methodology of the present invention describes the dynamic biochemical repair process that emerges in response to vascular injury and allows us to incorporate hypothetical inhibitors/enhancers at their proposed sites of interaction, thus providing an assessment of their efficacy.
  • Tf/lipid reagent The preparation of the Tf/lipid reagent was performed. 1,2- Dioleolyl-s «-Glycero-3-Phospho-L-Serine (PS) and l,2-Dioleoyl-.sw-Glycero-3- Phosphocholine (PC) were purchased from Avanti Polar Lipids, Inc (Alabaster, AL), and EDTA was purchased from Sigma (St Louis, MO). Phospholipid vesicles (PCPS) composed of 75% PC and 25% PS and were prepared.
  • PS Dioleolyl-s «-Glycero-3-Phospho-L-Serine
  • PC l,2-Dioleoyl-.sw-Glycero-3- Phosphocholine
  • PCPS Phospholipid vesicles
  • Spectrozyme TH was purchased from American Diagnostica, Inc (Greenwich, CT) and D-Phe-Pro- ArgCH 2 Cl (FPRck) was prepared.
  • UFH 145 IU/mg
  • DAPA dansylarginine-N-[3-ethyl-l,5- pentanediyl] amide
  • DAPA was purchased from Haematologic Technologies Inc. (Essex Junction, VT) and fondaparinux (Fpx) (GlaxoSmithKline, Research Triangle Park, NC) was purchased from Fletcher Allen Health Care (Burlington, VT).
  • Benzenesulfonyl-D-Arg-Gly-Arg-ketothiazole (C921-78) was provided as a gift.
  • ELISA thrombin-AT (TAT) kit Enzygnost TAT was purchased from Behring (Marburg, Germany).
  • Computational Model The current mathematical model as described herein was used, which yields concentration versus time profiles for selected species when electronic mixtures of the procoagulants ffl, flX, fX, fVTI, fVIIa, fV, and fVTH and the anticoagulants TFPI and AT are exposed to picomolar concentrations of Tf.
  • C921-78 has been reported to specifically and reversibly inhibit fXa and fXa when associated with fVa on membranes (prothrombinase).
  • the authors initially proposed a one step reversible association model for the inhibition of fXa: the equilibrium was characterized by k on values between 10 7 to 10 8 M "1 s '1 and ko f r values from 10 "4 to 10 "5 s "1 , yielding a KD for fXa of ⁇ 14 pM and 22 pM for fXa in prothrombinase.
  • DAPA is a reversible, active site directed inhibitor of thrombin and meizothrombin (mlla), with a K D of 20-40 nM.
  • the k off for the DAPA thrombin complex is in the range of 0.04 to 0.07 s "1 .
  • thromboin concentration at each time point is given as the sum of ⁇ -thrombin and meizothrombin concentrations, reflecting the fact that both species cleave the chromogenic assay employed in the empirical assays.
  • Relipidated Tf at 5 pM final concentration was added to a mixture of ffl, fV, fV ⁇ , fVnia, fVm, ffX, fX, fXI, TFPI, and AT (all at mean physiologic concentrations) (43) in 20 mM HEPES/150 mM NaCl (HBS) with 2 mM CaCl 2 containing 2 ⁇ M PCPS. Pharmacologic agents were incorporated into the reaction mixture prior to addition of the Tf reagent.
  • Resupply was conducted at 20 min post Tf initiation by the addition of an equal volume of a freshly constituted, Tf free mixture containing proco factors, zymogens and inhibitors in HBS with 2 mM CaCl 2 and 2 ⁇ M PCPS to an ongoing reaction. Pharmacologic agents were present in the resupply mixture at twice their final concentration.
  • Thrombin generation over time was measured in a chromogenic assay using Spectrozyme TH and a THERMO max microplate reader (Molecular Devices Corp., Menlo Park, CA).
  • rates of thrombin generation were calculated by reference to a standard curve relating thrombin concentration to rates of substrate hydrolysis in the presence of relevant DAPA concentrations.
  • the tubes are centrifuged and the supernatants are aliquoted and analyzed for TAT levels.
  • a subset of tubes containing either Fpx or C921-78 were initiated with 5 pM Tf reagent, quenched at 20 min and analyzed for TAT levels.
  • the CTI blood from the first draw clotted at 37 min
  • CTI blood from the second draw that was not used in the resupply experiment clotted after 35 min, demonstrating the absence of contact pathway contributions during the experimental time frame.
  • Fig. 27 presents the sets of equations describing the mechanisms of action of each of the anticoagulants used in this study and Fig. 28 presents the rate constants controlling the identified bimolecular interactions and the dosing ranges recommended for prophylactic and therapeutic applications. Details of modeling the various anticoagulant interactions are described herein and in the Methods section.
  • Prophylactic Anticoagulation There is shown in Figs. 3OA and 30 C computational assessments and in Figs'. 30B and D empirical assessments of the efficacy of each anticoagulant in suppressing the onset of Tf-initiated thrombin formation. In both empirical and computational studies, a 5 pM Tf reagent stimulus was used and thrombin generation assessed over 20 min. Results are presented that compare thrombin generation in the absence of anticoagulant ( ⁇ ) to that observed over the range of anticoagulant concentrations that drive the transition from partial to maximum suppression of thrombin formation.
  • the enhanced suppression of thrombin levels in the presence of UFH and DAPA has 2 mechanistic routes, distinguishable on the basis of prothrombin consumption: neutralizing free thrombin in the system as it is produced by prothrombinase ⁇ i.e., prothrombin is consumed) and blocking the Tf-initiated process from the onset by inhibiting the initial thrombin dependent activation of fV and fVTH effectively preventing prothrombin consumption.
  • Computational data are presented in terms of functional thrombin concentrations unless the time courses of thrombin production and prothrombin consumption diverge. To facilitate comparisons between modeling and empirical studies, the same symbol is used for a given concentration of an anticoagulant.
  • Fpx The computational assessment of Fpx efficacy predicted that Fpx concentrations in the range of 0.5 ⁇ M, representing -15% occupancy of the AT pool at the 5 onset of the reaction, would be required to suppress thrombin generation over 20 min (Fig. 3OC, ⁇ ). Lower levels of Fpx in the reaction, 125 nM (Fig. 30C, D) and 250 nM (Fig. 3OC,: •) delayed the onset, maximum rate and maximum levels of the thrombin produced when compared to the control (Fig. 30C, ⁇ ). Evaluation of the efficacy of Fpx in the i synthetic coagulation proteome showed a similar concentration dependence, although the
  • C921-78 proved somewhat more effective than computationally predicted in displacing the onset of thrombin generation, but did show the predicted minimal effects on maximum rate and concentrations of Ha. Direct comparison of these results to clinical usage recommendations for C921-78 is not possible; with another direct fXa inhibitor, an effective prophylaxis range between 450 and 1800 times the K D for fXa has been reported.
  • UFH The computational assessment of UFH efficacy indicated that complete suppression of the resupply response was not achievable.
  • thrombin levels reached 22 nM within 5 s and were ⁇ 10 nM after 60 s.
  • Even at a UFH concentration sufficient to bind 100% of the available AT (-15.8 U/mL) thrombin levels were predicted to reach ⁇ 12 nM within 5 s and be at ⁇ 7 nM after 60 s.
  • the computational assessment predicts that reaching the level of suppression achieved in the Tf- initiated process (e.g., Fig. 30A, •) requires -200 fold higher concentrations of UFH.
  • Fpx - The computational assessment of Fpx efficacy indicated that there was no Fpx concentration that would lead to complete suppression of the resupply response. Increasing suppression was predicted as the percentage of the AT pool complexed with Fpx was raised (e.g., from 9% (Fig. 32C, •) to 37% (Fig. 32C, A). However at a concentration representing 100% saturation of the available AT pool (Fig. 32C, : ⁇ ), thrombin levels still approached 75 nM at 1 min, ⁇ 30% of the level achieved in the absence of anticoagulant (Fig. 32C, ⁇ ).
  • Fpx efficacy in the synthetic coagulation proteome did not differ greatly from the computational predictions, showing a similar sensitivity to the concentration of Fpx, with a similar limit to the overall susceptibility of the resupply response to Fpx-mediated inhibition.
  • Fpx (Fig. 32D, ⁇ )
  • thrombin levels at 1 min reached ⁇ 70 nM, or 35% of the maximum level observed in the absence of anticoagulants (Fig. 32D, ⁇ ).
  • Direct fXa inhibitor C921-78
  • the computational assessment of C921-78 efficacy indicated that effective suppression of the resupply response could be achieved.
  • Fig. 33 A shows thrombin generation time course after resupply in the presence of 1 nM ( ⁇ ), 10 nM (•), 40 nM (A) and 160 nM (O) C921-78. Reduced but significant thrombin generation is indicated at 10 nM, the concentration that is effective in suppressing the Tf-initiated process. At 160 nM C921-78, thrombin generation is predicted to remain below 5 nM over 20 min. C921-78 efficacy in the synthetic coagulation proteome exhibited similar concentration dependence. Fig.
  • 33B shows representative thrombin generation time courses after resupply in the presence of 40 nM (A) and 160 nM (O).
  • A 40 nM
  • O 160 nM
  • 34A presents a resupply time course (truncated for presentation) in the absence of anticoagulant.
  • Lanes (a to c) display the relative mobility of ffl, reaction intermediates (Fl.2A, pre-1) and end products (Fl.2, B chain).
  • Lane (d) thereof represents the 20 min time point, which was sampled immediately prior to resupply. It shows the expected absence of prothrombin. Since the primary antibody used does not recognize thrombin when complexed with AT (TAT), the major product species after 20 min is not detected. In the absence of anticoagulant, prothrombin consumption is greater than 95% complete at 1 min post resupply, with the increase in the B chain reflecting the presence of active thrombin immediately after resupply.
  • Fig. 34B presents a composite of 5 immunoblots, showing for each one only the lanes visualizing the changes in prothrombin concentration for 15 min following resupply.
  • Prothrombin consumption can be seen to be most effectively blocked in the presence of 160 nM C921-78 and 0.5 LVmL L 1 FH, consistent with the low levels of thrombin measured (Figs. 32B, ⁇ and 33B, O).
  • Densitometric analyses indicated that blockade by C921- 78 was somewhat more effective, allowing -10% prothrombin consumption at 15 min compared to ⁇ 20% in the presence of 0.5 LVmL UFH.
  • Fig. 35 presents a representative experiment assessing the performance of C921-78 and Fpx. The initial time course of TAT complex formation in the absence of any anticoagulant after stimulation with 5 pM Tf reagent is presented ( ⁇ ).
  • fTX/DCa inhibitors have showed promise, achieving effective anticoagulation and a reduced risk of bleeding compared to UFH(14).
  • Figs. 36A,B computational assessments of the efficacy of a hypothetical direct flXa inhibitor in suppressing both Tf-initiated thrombin formation (Fig. 36A) and thrombin formation following resupply (Fig. 36B).
  • G20210A factor V Leiden and factor XHI Val34Leu. Dr. Simon Body, at the Brigham and Women's Hospital in Boston, has a cohort of 1700 patients who underwent coronary artery bypass surgery and for whom have a recorded postoperative blood loss or thrombosis. We evaluated TFPI for them, and in collaboration will evaluate computational thrombin generation in a thrombosis and bleeding cohort based upon their FVIH, FII, TFPI and AT levels.
  • Hemophilia A Hemophilia A, an X-linked disease with an estimated prevalence of I in 10,000 males [Hoyer LW. Hemophilia A. N Engl J Med 1994; 330: 38-47], is characterized by the decrease or absence of functional FVIII. It is one of the most extensively studied hemorrhagic disorders, ranging from clinical observations and treatment regimens, from biochemical and molecular analyses, to investigative gene therapy. Within the hemophilia A population, phenotypic heterogeneity with respect to clinical severity is seen. Although, the potential to bleed is diagnosed by traditional coagulation assays the hemorrhagic pathology and its' clinical management are not always accurately predicted by these same assays.
  • Example 5 In vitro fertilization (IVF) is a well-documented risk factor for thromboembolic complications [Chan WS, Dixon ME. The "ART" of thromboembolism: A review of assisted reproductive technology and thromboembolic complications. Thromb Res 2008;121:713-26]. Suspected complications have included jugular venous thrombosis, and fatal cerebral infarction.
  • Possible plasma factor compositional reasons for the link between IVF and thrombotic events include activated protein C resistance, decreased protein S and antithrombin (AT), and increased fibrinogen and factor (F) VtII [Biron C, Galtier-Dereure F, Rabesandratana H, Bernard I, Aguilar-Martinez P, Schved JF, et al. Hemostasis parameters during ovarian stimulation for in vitro fertilization: results of a prospective study. Fertil Steril 1997;67: 104-9; Curvers J, Nap AW, Thomassen MC, Nienhuis SJ, Hamulyak K, Evers JL, et al. Effect of in vitro fertilization treatment and subsequent pregnancy on the protein C pathway.
  • thrombin a key enzyme in blood coagulation
  • Previous studies on citrated plasma using calibrated automated thrombography showed that women using oral contraceptives have shorter thrombin generation lag times and higher peak height.
  • women on oral contraceptives also had an increased rate and maximum level of thrombin generation in response to a 5 pM tissue factor (TF) stimulus than women not using oral contraceptives [Brummel-Ziedins KE, Vossen CY, Butenas S, Mann KG, Rosendaal FR. Thrombin generation profiles in deep venous thrombosis. J Thromb Haemost 2005;3: 2497— 505].
  • TF tissue factor
  • Harnett et al. noticed a significant decrease in clot time in women undergoing IVF using thrombelastography [Harnett MJ, Bhavani-Shankar K, Datta S, Tsen LC. hi vitro fertilization-induced alterations in coagulation and fibrinolysis as measured by thromboelastography. Anesth Analg 2002;95: 1063-6 table].
  • Factor levels were measured on each of the individuals before and after stimulation for the coagulation proteins FJJ, FV, FVII, FVIII, FIX, FX, AT and total tissue factor pathway inhibitor (TFPI).
  • FJJ, FV, FVU, FVIJJ, FIX and FX were measured by using a Stago STA-R, in which the subjects plasma is added to plasma deficient in the factor to be measured and the degree of correction of clotting time is determined. The degree of the correction is compared with that obtained by adding a normal plasma to the same system.
  • AT is measured using a STA- Stachrom AT Colorimetric assay and total TFPI was measured by ELISA (Diagnostica Stago, France).
  • Thrombin generation was evaluated by parameters that reflect the global qualities of the thrombin profile which includes: time to 10 nM thrombin (clot time, CT), maximum level (MaxL) and rate (MaxR) of thrombin generation and the times at which they are reached, TMaxL and TMaxR, respectively.
  • CT clot time
  • MaxL maximum level
  • MaxR rate
  • each coagulation protein or set of proteins
  • thrombin generation simulations had one (or more) of the proteins set to the level it was prior to gonadotropin stimulation, while leaving all others at post-stimulation levels.
  • adjusting the after stimulation levels of FVTII, AT and TFPI to the levels they were at before stimulation resulted in near identical thrombin generation curves to those generated when all proteins were at before stimulation (Fig. 40B).
  • the numerical model that was used for these studies includes key plasma pro- and anti- coagulants of the TF pathway to thrombin generation that are evaluated in clinical laboratories. This model correlates well to empirical models describing the TF pathway [Hockin MF, Jones KC, Everse SJ, Mann KG. A model for the stoichiometric regulation of blood coagulation. J Biol Chem 2002;277: 18322-33; Brummel-Ziedins K, Rivard GE, Pouliot RI, Butenas S, Gissel M, Parhami-Seren B, et al. Factor VTIa replacement therapy in factor VJJ deficiency.

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Abstract

La présente invention concerne des procédés permettant une recherche des risques hémostatiques, y-compris le risque de syndrome coronarien aigu (ACS). Ces procédés consistent à acquérir une composition de plasma sanguin correspondant à échantillon biologique prélevé sur un sujet, à déterminer des paramètres en lien avec la coagulation sanguine, à simuler in vitro la coagulation sanguine au moyen des paramètres déterminés, à comparer à une référence le résultat d'une telle simulation, et enfin à déduire de cette comparaison le risque hémostatique. Pour d'autres modes de réalisation, de tels procédés consistent à se s'appuyer sur une telle comparaison pour sélectionner un schéma ou protocole thérapeutique. Pour encore d'autres modes de réalisation, de tels procédés consistent en plus à vérifier l'efficacité de médicaments, produits pharmaceutiques et analogues d'un protocole thérapeutique donné, notamment par simulation in vitro de l'application de tels médicaments, produits pharmaceutiques, et analogues.
PCT/US2009/003148 2008-05-20 2009-05-20 Prédiction du risque hémostatique en fonction de la composition du plasma WO2009142744A2 (fr)

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EP3422011A1 (fr) * 2017-06-28 2019-01-02 Koninklijke Philips N.V. L'estimation de valeur de paramètre dans un système de coagulation
RU2720241C1 (ru) * 2019-11-17 2020-04-28 Федеральное государственное бюджетное учреждение "Уральский научно-исследовательский институт охраны материнства и младенчества" Министерства здравоохранения Российской Федерации (ФГБУ "НИИ ОММ" Минздрава России) Способ прогноза гиперкоагуляционных осложнений гестации после переноса "свежих" эмбрионов в программах ЭКО

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WO2015091115A1 (fr) 2013-12-19 2015-06-25 Koninklijke Philips N.V. Procédé de détermination du risque hémostatique d'un sujet
CN105829888A (zh) * 2013-12-19 2016-08-03 皇家飞利浦有限公司 用于确定对象的止血风险的方法
EP3422011A1 (fr) * 2017-06-28 2019-01-02 Koninklijke Philips N.V. L'estimation de valeur de paramètre dans un système de coagulation
WO2019002324A1 (fr) * 2017-06-28 2019-01-03 Koninklijke Philips N.V. Estimation de valeur de paramètre dans un système de coagulation
RU2720241C1 (ru) * 2019-11-17 2020-04-28 Федеральное государственное бюджетное учреждение "Уральский научно-исследовательский институт охраны материнства и младенчества" Министерства здравоохранения Российской Федерации (ФГБУ "НИИ ОММ" Минздрава России) Способ прогноза гиперкоагуляционных осложнений гестации после переноса "свежих" эмбрионов в программах ЭКО

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